Emotion recognition in EEG signals using deep learning methods: A review

M Jafari, A Shoeibi, M Khodatars… - Computers in Biology …, 2023 - Elsevier
Emotions are a critical aspect of daily life and serve a crucial role in human decision-making,
planning, reasoning, and other mental states. As a result, they are considered a significant …

[HTML][HTML] Review of the emotional feature extraction and classification using EEG signals

J Wang, M Wang - Cognitive robotics, 2021 - Elsevier
As a subjectively psychological and physiological response to external stimuli, emotion is
ubiquitous in our daily life. With the continuous development of the artificial intelligence and …

Motor imagery EEG classification algorithm based on CNN-LSTM feature fusion network

H Li, M Ding, R Zhang, C Xiu - Biomedical signal processing and control, 2022 - Elsevier
Motor imagery brain-computer interface (MI-BCI) provides a novel way for human-computer
interaction. Traditional neural networks often use serial structure to extract spatial features …

Automated feature extraction on AsMap for emotion classification using EEG

MZI Ahmed, N Sinha, S Phadikar, E Ghaderpour - Sensors, 2022 - mdpi.com
Emotion recognition using EEG has been widely studied to address the challenges
associated with affective computing. Using manual feature extraction methods on EEG …

[HTML][HTML] COVID-19 detection using chest X-ray images based on a developed deep neural network

Z Mousavi, N Shahini, S Sheykhivand, S Mojtahedi… - SLAS technology, 2022 - Elsevier
Aim Currently, a new coronavirus called COVID-19 is the biggest challenge of the human at
21st century. Now, the spread of this virus is such that mortality has risen strongly in all cities …

Differentiating brain states via multi-clip random fragment strategy-based interactive bidirectional recurrent neural network

S Zhang, E Shi, L Wu, R Wang, S Yu, Z Liu, S Xu, T Liu… - Neural Networks, 2023 - Elsevier
EEG is widely adopted to study the brain and brain computer interface (BCI) for its non-
invasiveness and low costs. Specifically EEG can be applied to differentiate brain states …

[HTML][HTML] Recording brain activity while listening to music using wearable EEG devices combined with Bidirectional Long Short-Term Memory Networks

J Wang, Z Wang, G Liu - Alexandria Engineering Journal, 2024 - Elsevier
Electroencephalography (EEG) signals are crucial for investigating brain function and
cognitive processes. This study aims to address the challenges of efficiently recording and …

Holistic approaches to music genre classification using efficient transfer and deep learning techniques

SK Prabhakar, SW Lee - Expert Systems with Applications, 2023 - Elsevier
With the rapid development of high-tech multimedia technologies, many musical resource
assets are available online and it has always triggered an interest in the classification of …

Deep learning for detecting multi-level driver fatigue using physiological signals: A comprehensive approach

M Peivandi, SZ Ardabili, S Sheykhivand, S Danishvar - Sensors, 2023 - mdpi.com
A large share of traffic accidents is related to driver fatigue. In recent years, many studies
have been organized in order to diagnose and warn drivers. In this research, a new …

Developing a deep neural network for driver fatigue detection using EEG signals based on compressed sensing

S Sheykhivand, TY Rezaii, S Meshgini, S Makoui… - Sustainability, 2022 - mdpi.com
In recent years, driver fatigue has become one of the main causes of road accidents. As a
result, fatigue detection systems have been developed to warn drivers, and, among the …